Mathematical Model

RubyScore transforms a wallet’s on-chain behavior into reputation signals using a multi-factor model that includes:

• Metric normalization (per chain and over time),

• Cross-chain aggregation,

• Component computation for quality / consistency / diversity (with anti-fraud controls).

Core signals (non-exhaustive)

• Amount on balance — current and averaged balance.

• Gas spent — total gas burned.

• Transactions with unique contracts — count of distinct contracts; diversification by category (DeFi / NFT / Bridge / DAO).

• Transactions on different days — active days.

• Transactions on different weeks — weekly distribution of activity.

• Transactions on different months — month-over-month activity stability.

• Transaction volume — notional volume, normalized per chain/category.

• Number of transactions — total count and call typology.

Additionally considered:

• Depth of action sequences (e.g., bridge → deposit → swap → LP → governance),

• Economic rationality (fee/volume ratios, asset retention),

• Temporal stability (regularity vs. one-off spikes),

• Pattern uniqueness (deviation from mass scripted patterns).

Scoring approach

RubyScore uses a proprietary scoring model: a composition of weighted, normalized metrics with anti-fraud rules. The output is calibrated to a unified scale and can be tuned for a specific product or ecosystem.

The final score indicates how closely a wallet’s behavior matches the ideal profile of a valuable audience for a given task or network.

This approach makes reputation verifiable, portable, and configurable—the foundation for trust and fair incentives in Web3.